Graph-based searching for data stream

ABSTRACT

A method, system, and computer program product for graph-based searching for one or more data streams is disclosed. A computer-implemented method comprises extracting a plurality of tuples from one or more data streams. The method further comprises generating a graph for the plurality of tuples in which a node represents a tuple of the plurality of tuples and an edge represents a correlation between the node and another node, and the edge is generated based at least partly on one or more predetermined queries for the one or more data streams. The method further comprises traversing the graph based on the one or more predetermined queries. Accordingly, embodiments of the present disclosure can improve the search speed by use of the graph-based searching for one or more data streams.

BACKGROUND

Embodiments of the present disclosure relate generally to informationsearching, and more specifically, to graph-based searching for one ormore data streams.

A data stream is a sequence of digitally encoded coherent signals (suchas data packets) used to transmit or receive information that is in theprocess of being transmitted, and the data stream is generallycontinuous and uninterrupted. Generally, in a scenario of Internet ofThings (IoT), there are many types of sensors to monitor an object or anenvironment from different dimensions. For example, a first sensor isused to collect a temperature data stream of a house which has severalrooms, a second sensor is used to collect a humidity data stream of thehouse, and a third sensor is used to collect an air quality index (AQI)data stream of the house.

Generally, data streams collected from different sensors need to beprocessed for data analysis, and the data analysis may focus on morethan one dimension. For example, these data streams may be analyzed inreal time to obtain an accurate result from multi-dimensions. To performthe data analysis, data from these different data streams need to becorrelated or linked together via a common attribution such as anidentification (ID) of a room. That is, searching the correlated datafrom different data streams is a basis of the subsequent data analysisfor these data streams.

SUMMARY

Example embodiments of the present disclosure provide a new approach forgraph-based searching for one or more data streams.

In an aspect, a computer-implemented method is provided. The methodcomprises extracting a plurality of tuples from one or more datastreams. The method further comprises generating a graph for theplurality of tuples in which a node represents a tuple of the pluralityof tuples and an edge represents a correlation between the node andanother node, and the edge is generated based at least partly on one ormore predetermined queries for the one or more data streams. The methodfurther comprises traversing the graph based on the one or morepredetermined queries.

In another aspect, a computing system is provided. The computing systemcomprises one or more processors, a memory coupled to at least one ofthe processors, and a set of computer program instructions stored in thememory and executed by at least one of the processors in order toperform actions of including extracting a plurality of tuples from oneor more data streams. The actions further include generating a graph forthe plurality of tuples in which a node represents a tuple of theplurality of tuples and an edge represents a correlation between thenode and another node, and the edge is generated based at least partlyon one or more predetermined queries for the one or more data streams.The actions further include traversing the graph based on the one ormore predetermined queries.

In yet another aspect, a computer program product for graph-basedsearching is provided. The computer program product comprises a computerreadable storage medium having program instructions embodied therewith,and the program instructions are executable by a device to cause thedevice to perform a method comprising: extracting a plurality of tuplesfrom one or more data streams; and generating a graph for the pluralityof tuples in which a node represents a tuple of the plurality of tuplesand an edge represents a correlation between the node and another node,wherein the edge is generated based at least partly on one or morepredetermined queries for the one or more data streams. The methodfurther comprises traversing the graph based on the one or morepredetermined queries.

According to embodiments of the present disclosure, the search speed forone or more data streams can be improved by use of the graph-basedsearching for the one or more data streams. That is, the correlationsearching for the one or more data streams may be transformed into atraversing process in a graph. Moreover, the edges in the graph may bereused during the traversing of the graph, which can reduce computingand memory consumption significantly. Since the nodes in the graph maybe traversed individually, embodiments of the present disclosure can beimplemented in a distributed environment, and the traversing actions atdifferent nodes may be performed in parallel and asynchronously.Furthermore, embodiments of the present disclosure provide a detailedway to generate a graph for the data streams and traverse the graph, bywhich both the search speed and the search accuracy can be ensured.

It is to be understood that the Summary is not intended to identify keyor essential features of embodiments of the present disclosure, nor isit intended to be used to limit the scope of the present disclosure.Other features of the present disclosure will become easilycomprehensible through the description below.

BRIEF DESCRIPTION OF THE DRAWINGS

Through the more detailed description of some embodiments of the presentdisclosure in the accompanying drawings, the above and other objects,features and advantages of the present disclosure will become moreapparent, wherein the same reference generally refers to the samecomponents in the embodiments of the present disclosure.

FIG. 1A depicts a cloud computing node according to an embodiment of thepresent disclosure;

FIG. 1B depicts a cloud computing environment according to an embodimentof the present disclosure;

FIG. 1C depicts abstraction model layers according to an embodiment ofthe present disclosure;

FIG. 2 is flowchart of a method for graph-based searching for one ormore data streams in accordance with embodiments of the presentdisclosure;

FIG. 3 is diagrams illustrating an example of a plurality of datastreams in accordance with embodiments of the present disclosure;

FIGS. 4A-4C are diagrams illustrating example processes for generating agraph for a plurality of data streams in accordance with embodiments ofthe present disclosure; and

FIGS. 5A-5D are diagrams illustrating example processes for traversingthe graph generated FIGS. 4A-4C based on predetermined queries inaccordance with embodiments of the present disclosure.

Throughout the drawings, the same or similar reference numeralsrepresent the same or similar elements.

DETAILED DESCRIPTION

Principle of the present disclosure will now be described with referenceto some example embodiments. It is to be understood that theseembodiments are described only for the purpose of illustration and helpthose skilled in the art to understand and implement the presentdisclosure, without suggesting any limitations as to the scope of thedisclosure. The disclosure described herein can be implemented invarious manners other than the ones describe below.

As used herein, the term “includes” and its variants are to be read asopen terms that mean “includes, but is not limited to.” The term “a” isto be read as “one or more” unless otherwise specified. The term “basedon” is to be read as “based at least in part on.” The term “oneembodiment” and “an embodiment” are to be read as “at least oneembodiment.” The term “another embodiment” is to be read as “at leastone other embodiment.”

In some examples, values, procedures, or apparatus are referred to as“lowest”, “best,” “minimum,” or the like. It will be appreciated thatsuch descriptions are intended to indicate that a selection among manyused functional alternatives can be made, and such selections need notbe better, smaller, or otherwise preferable to other selections.

Some preferable embodiments will be described in more detail withreference to the accompanying drawings, in which the preferableembodiments of the present disclosure have been illustrated. However,the present disclosure can be implemented in various manners, and thusshould not be construed to be limited to the embodiments disclosedherein.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1A, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the disclosuredescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12 or aportable electronic device such as a communication device, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1A, computer system/server 12 in cloud computing node10 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the disclosure.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the disclosure as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 1B, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1B are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 1C, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 1B) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 1C are intended to be illustrative only and embodiments ofthe disclosure are not limited thereto. As depicted, the followinglayers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents.

Examples of hardware components include: mainframes 61; RISC (ReducedInstruction Set Computer) architecture based servers 62; servers 63;blade servers 64; storage devices 65; and networks and networkingcomponents 66. In some embodiments, software components include networkapplication server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and graph-based searching 96.

Conventionally, the collected data streams are correlated together byuse of an index, and the index is generally able to accelerate thesearching process for the collected data streams. However, it needs toconsume a lot of computing resources and memory resources to build theindex, and the searching in the data streams by use of the index alwaysspends too much time. Moreover, since the search result is unpredicted,the traditional methods cannot be performed in a distributedenvironment.

In order to at least partially solve the above and other potentialproblems, a new approach for graph-based searching for one or more datastreams is provided. According to embodiments of the present disclosure,the search speed for one or more data streams can be improved by use ofthe graph-based searching for the one or more data streams. That is, thecorrelation searching for the one or more data streams may betransformed into a traversing process in a graph.

Moreover, the edges in the graph may be reused during the traversing ofthe graph, which can reduce computing and memory consumptionsignificantly. Since the nodes in the graph may be traversedindividually, embodiments of the present disclosure can be implementedin a distributed environment, and the traversing actions at differentnodes may be performed in parallel and asynchronously. Furthermore,embodiments of the present disclosure provide a detailed way to generatea graph for the data streams and traverse the graph, by which both thesearch speed and the search accuracy can be ensured.

Now some embodiments will be discussed. FIG. 2 is flowchart of a methodfor graph-based searching for one or more data streams in accordancewith embodiments of the present disclosure. It is to be understood thatthe method 200 may be performed by the processing unit 16 with referenceto FIG. 1.

At 202, a plurality of tuples are extracted from one or more datastreams. For example, the one or more data streams are collected in realtime from a plurality of data sensors, and a tuple may be a portion ofthe data in a data stream. As used herein, the term “tuple” mayrepresent a bit sequence or a text message such as a string. Forexample, the data stream may be a sequence of bits such as [ . . .00110001 10110011 00110100 . . . ], and a tuple may be a part of thesequence, such as [10110100]. The data stream may include one or morefields, each represents a characteristic such as temperature. Forexample, the example tuple [10110100] may be divided into three datasegments [101], [10], [100] according to a predefined rule, and datasegment [101] may represent a value of a temperature field, data segment[10] may represent a value of a humidity field, and data segment [100]may represent a value of an AQI field. Some implementations of action202 will be discussed below with reference to the following FIG. 3.

At 204, a graph for the plurality of tuples is generated. The graphgenerally includes at least two nodes and at least one edge between twonodes, and each node corresponds to each of the plurality of tuples, andthus the number of nodes in the graph is identical to the number of thetuples or the data streams. Each edge represents a correlation betweentwo nodes, and each edge is generated based on a predetermined query.The predetermined query may include a correction query related to theone or more data streams, and the predetermined query may be used toretrieve the correlation among the plurality of tuples extracted fromthe one or more data streams. For example, if the predetermined queryindicates that two nodes have a correlation search and the correlatedvalues satisfy a predetermined correlation condition, the edge betweenthe two nodes is generated. In this way, all the data correlations arecombined into the edges in the graph. In some embodiments, the queriesfor predetermined types of data streams may be changeable during theruntime, and the generated graph may be updated based on the changedqueries. For example, the constructed graph will be updated according tothe latest queries during the runtime, and new tuples from the one ormore data streams will be used to construct the updated graph for thesubsequent traversing. Some implementations of action 204 will bediscussed below with reference to the following FIGS. 4A-4B.

At 206, the graph is traversed based on the predetermined queries. Upongenerating all the nodes and edges in the graph, the graph may be usedto process the predetermined queries. For example, if two nodes have anedge associated with a specific query, the specific query may be enabledto traverse the edge in the graph. In this way, the corresponding searchresult may be generated directly without any searching of the index.Some implementations of action 206 will be discussed below withreference to the following FIGS. 5A-5D.

According to the method 200 of the present disclosure, the graph isconstructed to take place of the index, and a query is performed bytraversing one or more edges in the graph, and thus it reduces the timefor the query significantly, thereby improving the search speed for oneor more data streams. According to the method 200 of the presentdisclosure, the edge in the graph can directly represent a correlationbetween two tuples in data streams, and thus there is no need to searchthe index for the data streams. As such, the performance of the datacorrelation searching will be improved significantly in the case thatdepth of correlation searching increases because the searching timecomplexity according to embodiments of the present disclosure isconstant for each data correlation.

FIG. 3 is a diagram 300 illustrating an example of a plurality of datastreams in accordance with embodiments of the present disclosure. Itwill be understood that the example processes in FIG. 3 may be regardedas a specific implementation of action 202 in the method 200 withrespect to FIG. 2.

As shown in FIG. 3, there are four data streams that are continuouscollected from a plurality of sensors, for example data stream 310(simply referred to as S1), data stream 320 (simply referred to as S2),data stream 330 (simply referred to as S3), and data stream 340 (simplyreferred to as S4). In some embodiments, some or all the data streamsmay be in a bit sequence format, and the meaning of each bit in the bitsequence may be predetermined. Alternatively, some or all of the datastreams may be in a text format such as an Extensive Markup Language(XML) format, a Comma-Separated Values (CSV) format, or a JavaScriptObject Notation (JSON) format. If not all the data streams are in thesame format, some format conversion may be applied to some data streamsin order to obtain a unified data format among the data streams.

As shown in FIG. 3, the data stream 310 at least includes data segments311-316, the data stream 320 at least includes data segments 321-328,the data stream 330 at least includes data segments 331-336, and thedata stream 340 at least includes data segments 341-348. Data indifferent data streams may be correlated through a field, as usedherein, the term “field” represents a characteristic such astemperature. For example, the data stream 310 includes quite a lot ofdata segments such as data segments 311-316, but it merely containsthree fields such as a temperature field, a humidity field and an AQIfield. The data segment 311 and the data segment 314, which are two datasegments, belong to a same field such as the temperature field, the datasegment 312 and the data segment 315 belong to the humidity field, andthe data segment 313 and the data segment 316 belong to the AQI field.That is, each filed may contain a plurality of data segments whichrepresent a same characteristic.

As shown in FIG. 3, for example, the data segment 314 (simply referredto as S1.a) in the data stream 310 and the data segment 326 (simplyreferred to as S2.a) in the data stream 320 are associated with a firstfield (i.e., “a”), the data segment 327 (simply referred to as S2.b) inthe data stream 320 and the data segment 336 (simply referred to asS3.b) in the data stream 330 are associated with a second field (i.e.,“b”), and the data segment 315 (simply referred to as S1.c) in the datastream 310 and the data segment 348 (simply referred to as S4.c) in thedata stream 320 are associated with a third field (i.e., “c”).

In some embodiments, a predetermined time window may be applied to theone or more data streams so as to extract the plurality of tuples. Forexample, the predetermined time window may indicate the recent fiveseconds, and the tuple may represent the data segment(s) collected inthe recent five seconds in the data stream. As shown in FIG. 3, by useof the predetermined time window, the tuple 319 which includes datasegments 314-316 is extracted from the data stream 310, the tuple 329which includes data segments 325-328 is extracted from the data stream320, the tuple 339 which includes data segments 334-336 is extractedfrom the data stream 330, and the tuple 349 which includes data segments345-348 is extracted from the data stream 340.

In some embodiments, one or more non-correlation queries are identifiedin the one or more predetermined queries, and each non-correlation querymay represent a query associated with a single tuple in the plurality oftuples. That is, if a specific query merely involves a single tuple, forexample, the value of the first field is above 3, this specific querymay be performed directly without bringing into the graph. Then, theplurality of tuples may be updated based on the one or morenon-correlation queries. In this way, the tuples are filteredpreliminarily through the one or more non-correlation queries in orderto improve the efficiency of the processing in the graph.

FIGS. 4A-4C are diagrams illustrating example processes for generating agraph for a plurality of data streams in accordance with embodiments ofthe present disclosure. It will be understood that the example processesin FIGS. 4A-4C may be regarded as a specific implementation of action204 in the method 200 with respect to FIG. 2, and the data streams maybe data streams 310, 320, 330 and 340 with respect to FIG. 3.

FIG. 4A is a diagram 400 illustrating an example process for determiningnodes and the attributes of the nodes in accordance with embodiments ofthe present disclosure. For example, upon extracting the plurality oftuples 319, 329, 339 and 349, a plurality of nodes may be generated inthe graph, and each node correspond to one tuple. As shown in FIG. 4A,the node 410 may correspond to the tuple 319, the node 420 maycorrespond to the tuple 329, the node 430 may correspond to the tuple339, and the node 440 may correspond to the tuple 349.

Next, the attributes of each node may be generated, which may include anidentification of the node, the content of the tuple, and an index of apredetermined query of one or more predetermined queries which isassociated with the tuple. According to embodiments of FIGS. 4A-4C, theone or more predetermined queries may be for example shown in table 1.

TABLE 1 example predetermined queries Index Predetermined queries 1 S1.a= S2.a 2 S2.b = S3.b 3 S1.c = S4.c

As shown in table 1, the predetermined query with an index “1” relatesto the field “a” of data streams S1 and S2, the predetermined query withan index “2” relates to the field “b” of data streams S2 and S3, and thepredetermined query with an index “c” relates to the field “c” of datastreams S1 and S4. That is, one predetermined query may involve onefield related to two data streams. It should be understood, although thequeries in table 1 merely illustrates the equality relationship, othermathematics relationships and mathematics functions may be alsopossible, for example, S1.a>S2.a, or abs(S1.a-S2.a)>3.0, as furtherdiscussed below.

For example, the set of attributes 415 of the node 410 is generated,which include the identification “ID1” that can be generated orextracted from the tuple, the content of the tuple 319, denoted as“TUPLE 1” which include the data segments 314-316. Moreover, the set ofattributes 415 may further include the index of the predeterminedqueries such as “1, 3”, because the tuple 319 (that is S1) is associatedwith the predetermined queries with indexes “1” and “3”, as shown inTable 1. As shown, the set of attributes 415 may also include a value ofa field corresponding to an index, for example, the value of the field“a” corresponding to the index “1” is 101, and the value of the field“c” corresponding to the index “3” is −2.0. In some embodiments, the setof attributes 415 may further include a search result that is used tostore the temporarily generated result. In this way, the set ofattributes 425 of the node 420, the set of attributes 435 of the node430, and the set of attributes 445 of the node 440 may also begenerated, as shown in FIG. 4A.

FIG. 4B is a diagram 450 illustrating an example process for correlatingnodes in the graph in accordance with embodiments of the presentdisclosure. As shown in 450, fields of all the tuples associated withthe predetermined queries are identified, and values of these fields areextracted from the tuples. For example, as shown in the dotted box 460,values 461-466 are extracted based on the predetermined queries in theabove Table 1.

Some groups 467-469 are then generated based on the values 461-466, andeach group satisfies a correlation condition for a predetermined query.The correlation condition may be determined based on whether two tuplesmeet a constraint in a predetermined query. As an example, thecorrelation condition may be defined as S1.a=S2.a, which means that avalue of a field “a” of a tuple from the data stream S1 is equal to thatof the field “a” of a tuple from the data stream S2. As another example,the correlation condition may be defined as S1.a>S2.a or S1.a<S2.a,which means that the value of the field “a” of a tuple from the datastream S1 is greater or less than that of the field “a” of a tuple fromthe data stream S2. In some embodiments, the correlation condition maybe defined as a function of a predetermined query, such asabs(S1.a-S2.a)>3.0, which means that the absolute value of thedifference between S1.a and S2.a is greater than 3.0. That is, ifabs(S1.a-S2.a)>3.0, the correlation condition is satisfied, and a groupmay be generated accordingly.

For example, in the case of the example predetermined queries in table1, the value “101” of the first field “a” are grouped together in thegroup 467, the value “1.0” of the second field “b” are grouped togetherin the group 468, and the value “−2.0” of the third field “c” aregrouped together in the group 469. Next, each group is transformed to anentry for an edge. For example, as shown in the dotted box 470, thegroup 467 is transformed to the entry 471 which indicates the node 410and the node 420 have an edge associated with the first field, the group468 is transformed to the entry 472 which indicates the node 420 and thenode 430 have an edge associated with the second field, and the group469 is transformed to the entry 473 which indicates the node 410 and thenode 440 have an edge associated with the third field.

FIG. 4C is a diagram 480 illustrating an example process for generatingedges in the graph in accordance with embodiments of the presentdisclosure. As shown in FIG. 4C, since the entry 471 indicates the node410 and the node 420 have an edge associated with the first field, edge481 associated with the first field is generated between the node 410and the node 420. Similarly, edge 482 associated with the second fieldis generated between the node 420 and the node 430, and edge 483associated with the third field is generated between the node 410 andthe node 440.

FIGS. 5A-5D are diagrams illustrating example processes for traversingthe graph based on predetermined queries in accordance with embodimentsof the present disclosure. It will be understood that the exampleprocesses in FIGS. 5A-5D may be regarded as a specific implementation ofaction 206 in the method 200 with respect to FIG. 2, and the graphs 500,530, 560 and 590 may be the graph 480 with respect to FIG. 4C.

FIG. 5A is a diagram 500 illustrating an example process for determininga start node in accordance with embodiments of the present disclosure.For example, a traverse event 505 is generated and broadcasted to allnodes in the graph. As shown in FIG. 500, the traverse event 505 isrelated to the queries with indexes “1” and “2” in the Table 1, that isS1.a=S2.a and S2.b=S3.b. Since the first query S1.a=S2.a first involves“S1,” the node 410 corresponding to the TUPLE 1 in the data stream “S1”is determined as the start node that is traversed first. Thus, the node410 will accept the traverse event 505 instead of nodes 420, 430 and440. Alternatively, the queries may be prepossessed and the node 410 maybe identified as the start node, and the traverse event 505 will only betransferred to the node 410. As shown in FIG. 5A, the traverse event 505may include an entry of result that is used to store temporarytraversing result. In some embodiments, if one or more queries in thetraverse event 505 are not found, the traverse event 505 may beterminated, and no data stream will be output.

FIG. 5B is a diagram 530 illustrating an example process fortransferring the traverse event between nodes in accordance withembodiments of the present disclosure. Since the first query S1.a=S2.ainvolves the edge 481, and the traverse event is transferred from thenode 410 to the node 420. Then, the traverse event 505 is updated to atraverse event 535 at the node 420, in which the first query that hasbeen traversed is deleted from the set of queries, and TUPLE 1 of thenode 410 that has been traversed is added into the query result.

Next, it is determined whether the updated set of queries is empty. Asshown in diagram 530 of FIG. 5B the query in the traverse event 535 isnot empty, and thus the process for transferring the traverse eventbetween nodes continues. Since the second query S2.b=S3.b involves theedge 482, and the traverse event is directly transferred from the node420 to the node 430. Then, as shown in diagram 560 of FIG. 5C, thetraverse event 535 is updated to a traverse event 565 at the node 430,in which the second query that has been traversed is deleted from theset of queries, and TUPLE 2 of the node 420 that has been traversed isadded into the query result.

Next, it is determined whether the updated set of queries is empty.Since the set of query in the traverse event 565 is empty, the queryresult in the traverse event is updated to add the TUPLE 3 of the node430, and the updated query result may be output. As shown in diagram 590of FIG. 5D, the traverse event 565 is finally updated to traverse event595, in which the search result is [“TUPLE 1”, “TUPLE 2”, “TUPLE 3”]. Atthis point, the traverse event is done and the outputted search resultis [“TUPLE 1”, “TUPLE 2”, “TUPLE 3”].

In some embodiments, if the same query is performed repeatedly, theedges in the graph may be reused during the traversing for the graph,which can reduce computing and memory consumption significantly.According to embodiments of the present disclosure, the nodes in thegraph may be traversed individually, and thus another search event withthe query S1.c=S4.c may be performed in parallel and asynchronously atthe local computing device or a remote device. That is, the traversingfor the graph according to embodiments of the present disclosure may beimplemented in a distributed environment, thereby improving parallelprocessing capability. Accordingly, embodiments of the presentdisclosure can improve the search speed for one or more data streams byuse of the graph-based searching for the one or more data streams.

The present disclosure may be a system, an apparatus, a device, amethod, and/or a computer program product at any possible technicaldetail level of integration. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++ or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The computer readable program instructions mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, devices(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams illustrate the architecture,functionality, and operation of possible implementations of systems,methods and computer program products according to various embodimentsof the present disclosure. In this regard, each block in the flowchartor block diagrams may represent a module, snippet, or portion ofinstructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reversed order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:extracting a plurality of tuples from one or more data streams;generating a graph for the plurality of tuples in which a noderepresents a tuple of the plurality of tuples and an edge represents acorrelation between the node and another node, the edge being generatedbased at least partly on one or more predetermined queries for the oneor more data streams; and traversing the graph based on the one or morepredetermined queries: wherein the traversing the graph comprises:determining a first node in the plurality of nodes which is traversedfirst; and transferring a traverse event from the first node to a secondnode based on the set of queries; updating a set of queries and a queryresult in the traverse event; determining whether the updated set ofqueries is empty; in response to determining that the updated set ofqueries is empty, updating the query result in the traverse event; andoutputting the updated query result.
 2. The method of claim 1, whereinthe generating the graph comprises: generating a plurality of attributesof the node, the plurality of attributes including an identification,content of the tuple, and an index of at least one predetermined queryof the one or more predetermined queries which is associated with thetuple.
 3. The method of claim 2, wherein the generating the graphfurther comprises: identifying a field of the tuple, the field of thetuple being associated with the predetermined query; extracting a valueof the field from the tuple; and generating the edge between the nodeand the other node based at least partly on the value of the field. 4.The method of claim 3, wherein the generating the edge between the nodeand the other node comprises: generating one or more groups based on avalue of each of one or more fields associated with the one or morepredetermined queries, one of the one or more groups satisfying acorrelation condition for one of the one or more predetermined queries;and generating the edge between the node and the other node based on theone or more groups.
 5. The method of claim 1, wherein the extracting aplurality of tuples from one or more data streams further comprises:identifying a non-correlation query in the one or more predeterminedqueries, the non-correlation query representing a query associated witha single tuple in the plurality of tuples; performing thenon-correlation query on the plurality of tuples; and updating theplurality of tuples based on the performing of the non-correlationquery.